The Internet, which is a worldwide network of interconnected networks and computers, makes available a wide variety of information through billions of hyperlinked “web page” to users. Among these masses of hard-linked content information, there is a tremendous amount of content-related information linkages that are missing and not explored due to the scale and intellectual complexity of finding such related information.
Also, when users browse content pages, they are eager to find more information that is related to the content they are reading. In the web information age, that kind of extended and enriching information that is best matched to the original content is often beyond the original authors' grasp.
The current art relies on human editors to comprehend the content or computer programs to search through the content to find some words to add related information to the original content.
There are at least two shortcomings in such a system, the first is that since the extending content is given simply corresponding to a whole document, while several subjects with different meaning exist in the whole document, there is no way to give the extending content respectively directed to these subjects. The second is since the extending content is given manually, the efficiency is low and the relevance is inaccurate.
The purpose of this application is to solve the problem, in which the content is divided into content regions, and the extending keywords relevant to the content of a current content region are found based on semantic relevance. The extending information provided by the keywords which are seamless extending of the initial content in semantics and integrated with the content, is often beyond expectation for a content creator or a user. This will significantly serve to help or expand the user's understanding of the content.